论文标题
触觉对象的姿势构成从第一触点的几何触点渲染的估计
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
论文作者
论文摘要
在本文中，我们提出了一种触觉姿势估计的方法，该方法是对已知对象的首次触摸的。首先，我们创建了一个从真实触觉观测值到接触形状的对象无关的地图。接下来，对于具有已知几何形状的新对象，我们在模拟中完全学习了一个量身定制的感知模型。为此，我们模拟了一个密集的物体姿势将在传感器上产生的密集对象姿势的接触形状。然后，鉴于从传感器输出获得的新接触形状，我们使用对比度学习纯粹在模拟中学习的对象特异性嵌入将其与预计集合进行匹配。 这会导致一种感知模型，该模型可以从单个触觉观察中定位对象。它还允许对姿势分布进行推理，并包括来自其他感知系统或多个联系人的其他姿势约束。我们为四个对象提供定量结果。我们的方法从独特的触觉观测中提供了高精度的姿势估计，同时回归姿势分布，以说明可能由不同对象姿势产生的接触形状。我们在多接触场景中进一步扩展并测试我们的方法，在多接触场景中，几个触觉传感器同时与对象接触。网站：http：//mcube.mit.edu/research/tactile_loc_first_touch.html
In this paper, we present an approach to tactile pose estimation from the first touch for known objects. First, we create an object-agnostic map from real tactile observations to contact shapes. Next, for a new object with known geometry, we learn a tailored perception model completely in simulation. To do so, we simulate the contact shapes that a dense set of object poses would produce on the sensor. Then, given a new contact shape obtained from the sensor output, we match it against the pre-computed set using the object-specific embedding learned purely in simulation using contrastive learning. This results in a perception model that can localize objects from a single tactile observation. It also allows reasoning over pose distributions and including additional pose constraints coming from other perception systems or multiple contacts. We provide quantitative results for four objects. Our approach provides high accuracy pose estimations from distinctive tactile observations while regressing pose distributions to account for those contact shapes that could result from different object poses. We further extend and test our approach in multi-contact scenarios where several tactile sensors are simultaneously in contact with the object. Website: http://mcube.mit.edu/research/tactile_loc_first_touch.html
